{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame({'first': ['Peter', 'Paul', 'Mary'], \n",
    "                   'last':['Doe', 'Miller', 'Johnson'],\n",
    "                    'weight': [68.3, 77.1, 56.7],\n",
    "                    'gender': ['m', 'm', 'f'],\n",
    "                    'ranking': [2, 3, 1],\n",
    "                    'dob': ['1958-05-03', '1958-12-01', '1959-03-20']})\n",
    "df.dob = pd.to_datetime(df.dob)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>first</th>\n",
       "      <th>last</th>\n",
       "      <th>weight</th>\n",
       "      <th>gender</th>\n",
       "      <th>ranking</th>\n",
       "      <th>dob</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Peter</td>\n",
       "      <td>Doe</td>\n",
       "      <td>68.3</td>\n",
       "      <td>m</td>\n",
       "      <td>2</td>\n",
       "      <td>1958-05-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Paul</td>\n",
       "      <td>Miller</td>\n",
       "      <td>77.1</td>\n",
       "      <td>m</td>\n",
       "      <td>3</td>\n",
       "      <td>1958-12-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Mary</td>\n",
       "      <td>Johnson</td>\n",
       "      <td>56.7</td>\n",
       "      <td>f</td>\n",
       "      <td>1</td>\n",
       "      <td>1959-03-20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   first     last  weight gender  ranking        dob\n",
       "0  Peter      Doe    68.3      m        2 1958-05-03\n",
       "1   Paul   Miller    77.1      m        3 1958-12-01\n",
       "2   Mary  Johnson    56.7      f        1 1959-03-20"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "first              object\n",
       "last               object\n",
       "weight            float64\n",
       "gender             object\n",
       "ranking             int64\n",
       "dob        datetime64[ns]\n",
       "dtype: object"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    68.3\n",
       "1    77.1\n",
       "2    56.7\n",
       "Name: weight, dtype: float64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.weight"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x26ea0286fc8>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot('dob', 'weight', marker='*', lw=0, ms=15)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
